Recent advances in electricity price forecasting: A review of probabilistic forecasting

Jakub Nowotarski and Rafał Weron

Renewable and Sustainable Energy Reviews, 2018, vol. 81, issue P1, 1548-1568

Abstract: Since the inception of competitive power markets two decades ago, electricity price forecasting (EPF) has gradually become a fundamental process for energy companies’ decision making mechanisms. Over the years, the bulk of research has concerned point predictions. However, the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply, demand and prices. Academics and practitioners alike have come to understand that probabilistic electricity price (and load) forecasting is now more important for energy systems planning and operations than ever before. With this paper we offer a tutorial review of probabilistic EPF and present much needed guidelines for the rigorous use of methods, measures and tests, in line with the paradigm of ‘maximizing sharpness subject to reliability’. The paper can be treated as an update and a further extension of the otherwise comprehensive EPF review of Weron [1] or as a standalone treatment of a fascinating and underdeveloped topic, that has a much broader reach than EPF itself.

Keywords: Electricity price forecasting; Probabilistic forecast; Reliability; Sharpness; Day-ahead market; Autoregression; Neural network (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (169)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1364032117308808
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:81:y:2018:i:p1:p:1548-1568

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/bibliographic
http://www.elsevier. ... 600126/bibliographic

DOI: 10.1016/j.rser.2017.05.234

Access Statistics for this article

Renewable and Sustainable Energy Reviews is currently edited by L. Kazmerski

More articles in Renewable and Sustainable Energy Reviews from Elsevier
Bibliographic data for series maintained by Catherine Liu ().